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Um für die DAMA DQ-1220-Zertifizierungsprüfung berechtigt zu sein, müssen die Kandidaten mindestens zwei Jahre Erfahrung im Datenmanagement oder in einem verwandten Bereich haben. Sie müssen auch ein Schulungsprogramm absolvieren, das den Inhalt der Prüfung abdeckt, wie z. B. Damas Datenqualitätsfundamentalskurs. Die Prüfung selbst ist ein computergestützter Test, der aus 100 Multiple-Choice-Fragen besteht. Die Kandidaten haben zwei Stunden Zeit, um die Prüfung abzuschließen, und es ist eine vorübergehende Punktzahl von 70% erforderlich.
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119. Frage
A minimal super key is:
Antwort: E
Begründung:
A candidate key is a minimal super key. A super key is any set of attributes sufficient to uniquely identify an entity instance, but it may contain attributes that are unnecessary for uniqueness. A candidate key removes that redundancy: if any attribute is removed from the candidate key, the remaining attributes no longer uniquely identify the entity.
DAMA-DMBOK2 makes this distinction explicitly: a candidate key is a minimal set of one or more attributes identifying an entity instance, and "minimal" means that no subset of the candidate key can perform the same unique-identification function.
Option B is incomplete because a set can uniquely identify an entity while still containing unnecessary attributes; that would qualify as a super key but not necessarily a minimal super key. A surrogate key is different: it is an artificial identifier introduced primarily for technical identification. Foreign-key composition and physical index structures likewise do not define candidate-key minimality.
This concept has direct Data Quality implications. Correctly defined candidate and primary keys support uniqueness and integrity, prevent duplicate entity instances, enable reliable referential relationships, and improve entity matching within Master Data Management. Key definitions should also be captured as structural metadata so profiling and quality rules can consistently test duplicate and orphan conditions.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Data Modeling and Design; Keys; Candidate Keys; Chapter 13 - Uniqueness and Integrity; Metadata Management; Master Data Management.
120. Frage
In data modelling practice, entities are linked by:
Antwort: A
Begründung:
In a data model, entities are linked by relationships. An entity represents a distinguishable business concept or thing about which the organization stores information, while a relationship expresses how one entity is associated with another.
For example, a Customer places an Order, an Order contains Order Lines, and a Product appears on an Order Line. Relationships therefore capture business semantics rather than merely physical implementation details.
Cardinality is an important characteristic of a relationship because it specifies how many instances of one entity may or must be associated with instances of another; however, cardinality is not itself the general mechanism by which entities are linked. DAMA-oriented modelling material identifies relationships as the correct linkage concept.
Indexes and triggers are physical database mechanisms. Processes describe business activities and may interact with entities, but they do not define entity-to-entity structure within a data model.
The Data Quality implications are significant. Properly defined relationships establish expectations for referential integrity. For example, an Order referencing a nonexistent Customer indicates an integrity defect.
Profiling can test orphaned foreign keys, invalid relationship cardinalities, and inconsistent associations.
Relationships and their cardinalities should therefore be documented as metadata and enforced through appropriate database, application, or quality controls.
Reference Topics: DAMA-DMBOK2 Chapter 5 - Entities, Relationships and Cardinality; Logical Data Modeling; Chapter 13 - Integrity and Consistency; Metadata Management.
121. Frage
A dashboard reports that Customer_Email Completeness is 98.5%, while the approved threshold is 99.5%.
What should the 98.5% figure be interpreted as?
Antwort: B
Begründung:
The 98.5% value is a measured Data Quality result, and because it is below the approved 99.5% threshold, it represents a threshold breach.
A Data Quality metric quantifies observed performance. A threshold establishes the level of performance the business has agreed is acceptable. These should not be confused. The metric answers how are we performing?; the threshold answers is that performance acceptable?
The 98.5% completeness result says nothing about whether the populated email addresses are accurate. A field can achieve high completeness while containing incorrect or invalid values. Other rules may therefore be required for email syntax, uniqueness, deliverability, or factual correctness.
A threshold breach should trigger a predefined response appropriate to the criticality of the data. This may involve investigation, escalation, remediation, trend review, or root-cause analysis.
Metrics, calculation logic, measurement population, thresholds, ownership, and escalation rules should be documented as metadata so dashboard results remain interpretable and reproducible.
DAMA's framework explicitly places metrics, error detection, cleansing, and quality management within Data Quality Management.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Metrics; Thresholds; Scorecards; Completeness; Monitoring.
122. Frage
Critical Data is most often used in:
Antwort: C
Begründung:
All of these is correct because DAMA-DMBOK2 defines criticality according to the business use and impact of the data rather than by a particular technical characteristic. Critical Data Elements are commonly associated with regulatory, financial, or management reporting; operational business needs; measurement of product quality and customer satisfaction; and strategic initiatives such as competitive differentiation. DAMA's revised Chapter 13 also explicitly adds and clarifies the concept of Critical Data Elements within the Data Quality framework.
The purpose of identifying critical data is prioritization. Organizations cannot apply identical levels of profiling, monitoring, stewardship, remediation, and control to every data element. Data whose failure could create substantial operational, financial, legal, regulatory, customer, or reputational impact receives greater management attention.
For example, values feeding regulatory reports require stringent accuracy and traceability; operational data may require high availability and timeliness; customer-satisfaction measures require reliable and consistent inputs; and strategically important data can affect competitive decisions. Current DAMA-aligned material confirms these same usage categories for Critical Data Elements.
Once Critical Data Elements are identified, they should be linked to business definitions, Data Owners and Stewards, quality dimensions, measurable rules, lineage, authoritative sources, thresholds, and issue- management processes.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Critical Data Elements; Business Drivers; Data Quality Requirements; Prioritization; Data Governance; Metadata Management.
123. Frage
A report displaying birth date contains possible, but incorrect values. What is a possible explanation?
Antwort: E
Begründung:
The critical wording is "possible, but incorrect values." This describes values that satisfy basic syntactic or domain validation-they look like legitimate dates-but do not accurately represent the real-world attribute defined by the field.
If two systems contribute data and one maps marriage date into the birth-date field, the resulting values can be perfectly valid calendar dates while being semantically incorrect as birth dates. This is principally an Accuracy defect, because DAMA defines accuracy in terms of how correctly data represents the real-world object or event it is intended to describe. It may also expose a consistency and integration-mapping problem between source systems. DAMA's quality framework distinguishes accuracy from completeness: data can be populated and formally valid while still being factually wrong.
Missing values would primarily produce a Completeness defect rather than populated-but-incorrect values.
Two correctly mapped systems would not inherently explain the problem. An offset or technical date representation could create transformation problems, but the scenario most directly illustrates semantic mis- mapping between data elements.
The appropriate remediation is therefore not simple cleansing alone. Metadata mappings, source-to-target specifications, lineage, business definitions, and integration rules should be corrected at the root cause.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Accuracy, Completeness and Consistency; Root-Cause Remediation; Data Profiling; Metadata Management; Data Integration and Interoperability.
124. Frage
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